Prediction of lithium response using genomic data
Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with...
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| Autor*in: |
William Stone [verfasserIn] Abraham Nunes [verfasserIn] Kazufumi Akiyama [verfasserIn] Nirmala Akula [verfasserIn] Raffaella Ardau [verfasserIn] Jean-Michel Aubry [verfasserIn] Lena Backlund [verfasserIn] Michael Bauer [verfasserIn] Frank Bellivier [verfasserIn] Pablo Cervantes [verfasserIn] Hsi-Chung Chen [verfasserIn] Caterina Chillotti [verfasserIn] Cristiana Cruceanu [verfasserIn] Alexandre Dayer [verfasserIn] Franziska Degenhardt [verfasserIn] Maria Del Zompo [verfasserIn] Andreas J. Forstner [verfasserIn] Mark Frye [verfasserIn] Janice M. Fullerton [verfasserIn] Maria Grigoroiu-Serbanescu [verfasserIn] Paul Grof [verfasserIn] Ryota Hashimoto [verfasserIn] Liping Hou [verfasserIn] Esther Jiménez [verfasserIn] Tadafumi Kato [verfasserIn] John Kelsoe [verfasserIn] Sarah Kittel-Schneider [verfasserIn] Po-Hsiu Kuo [verfasserIn] Ichiro Kusumi [verfasserIn] Catharina Lavebratt [verfasserIn] Mirko Manchia [verfasserIn] Lina Martinsson [verfasserIn] Manuel Mattheisen [verfasserIn] Francis J. McMahon [verfasserIn] Vincent Millischer [verfasserIn] Philip B. Mitchell [verfasserIn] Markus M. Nöthen [verfasserIn] Claire O’Donovan [verfasserIn] Norio Ozaki [verfasserIn] Claudia Pisanu [verfasserIn] Andreas Reif [verfasserIn] Marcella Rietschel [verfasserIn] Guy Rouleau [verfasserIn] Janusz Rybakowski [verfasserIn] Martin Schalling [verfasserIn] Peter R. Schofield [verfasserIn] Thomas G. Schulze [verfasserIn] Giovanni Severino [verfasserIn] Alessio Squassina [verfasserIn] Julia Veeh [verfasserIn] Eduard Vieta [verfasserIn] Thomas Trappenberg [verfasserIn] Martin Alda [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Übergeordnetes Werk: |
In: Scientific Reports - Nature Portfolio, 2011, 11(2021), 1, Seite 10 |
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| Übergeordnetes Werk: |
volume:11 ; year:2021 ; number:1 ; pages:10 |
| Links: |
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| DOI / URN: |
10.1038/s41598-020-80814-z |
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| Katalog-ID: |
DOAJ059716029 |
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| 520 | |a Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. | ||
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10.1038/s41598-020-80814-z doi (DE-627)DOAJ059716029 (DE-599)DOAJ3122c8062c9b4f39a9a4aa0e6220ae8c DE-627 ger DE-627 rakwb eng William Stone verfasserin aut Prediction of lithium response using genomic data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. Medicine R Science Q Abraham Nunes verfasserin aut Kazufumi Akiyama verfasserin aut Nirmala Akula verfasserin aut Raffaella Ardau verfasserin aut Jean-Michel Aubry verfasserin aut Lena Backlund verfasserin aut Michael Bauer verfasserin aut Frank Bellivier verfasserin aut Pablo Cervantes verfasserin aut Hsi-Chung Chen verfasserin aut Caterina Chillotti verfasserin aut Cristiana Cruceanu verfasserin aut Alexandre Dayer verfasserin aut Franziska Degenhardt verfasserin aut Maria Del Zompo verfasserin aut Andreas J. Forstner verfasserin aut Mark Frye verfasserin aut Janice M. Fullerton verfasserin aut Maria Grigoroiu-Serbanescu verfasserin aut Paul Grof verfasserin aut Ryota Hashimoto verfasserin aut Liping Hou verfasserin aut Esther Jiménez verfasserin aut Tadafumi Kato verfasserin aut John Kelsoe verfasserin aut Sarah Kittel-Schneider verfasserin aut Po-Hsiu Kuo verfasserin aut Ichiro Kusumi verfasserin aut Catharina Lavebratt verfasserin aut Mirko Manchia verfasserin aut Lina Martinsson verfasserin aut Manuel Mattheisen verfasserin aut Francis J. McMahon verfasserin aut Vincent Millischer verfasserin aut Philip B. Mitchell verfasserin aut Markus M. Nöthen verfasserin aut Claire O’Donovan verfasserin aut Norio Ozaki verfasserin aut Claudia Pisanu verfasserin aut Andreas Reif verfasserin aut Marcella Rietschel verfasserin aut Guy Rouleau verfasserin aut Janusz Rybakowski verfasserin aut Martin Schalling verfasserin aut Peter R. Schofield verfasserin aut Thomas G. Schulze verfasserin aut Giovanni Severino verfasserin aut Alessio Squassina verfasserin aut Julia Veeh verfasserin aut Eduard Vieta verfasserin aut Thomas Trappenberg verfasserin aut Martin Alda verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 10 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:10 https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c kostenfrei https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 10 |
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10.1038/s41598-020-80814-z doi (DE-627)DOAJ059716029 (DE-599)DOAJ3122c8062c9b4f39a9a4aa0e6220ae8c DE-627 ger DE-627 rakwb eng William Stone verfasserin aut Prediction of lithium response using genomic data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. Medicine R Science Q Abraham Nunes verfasserin aut Kazufumi Akiyama verfasserin aut Nirmala Akula verfasserin aut Raffaella Ardau verfasserin aut Jean-Michel Aubry verfasserin aut Lena Backlund verfasserin aut Michael Bauer verfasserin aut Frank Bellivier verfasserin aut Pablo Cervantes verfasserin aut Hsi-Chung Chen verfasserin aut Caterina Chillotti verfasserin aut Cristiana Cruceanu verfasserin aut Alexandre Dayer verfasserin aut Franziska Degenhardt verfasserin aut Maria Del Zompo verfasserin aut Andreas J. Forstner verfasserin aut Mark Frye verfasserin aut Janice M. Fullerton verfasserin aut Maria Grigoroiu-Serbanescu verfasserin aut Paul Grof verfasserin aut Ryota Hashimoto verfasserin aut Liping Hou verfasserin aut Esther Jiménez verfasserin aut Tadafumi Kato verfasserin aut John Kelsoe verfasserin aut Sarah Kittel-Schneider verfasserin aut Po-Hsiu Kuo verfasserin aut Ichiro Kusumi verfasserin aut Catharina Lavebratt verfasserin aut Mirko Manchia verfasserin aut Lina Martinsson verfasserin aut Manuel Mattheisen verfasserin aut Francis J. McMahon verfasserin aut Vincent Millischer verfasserin aut Philip B. Mitchell verfasserin aut Markus M. Nöthen verfasserin aut Claire O’Donovan verfasserin aut Norio Ozaki verfasserin aut Claudia Pisanu verfasserin aut Andreas Reif verfasserin aut Marcella Rietschel verfasserin aut Guy Rouleau verfasserin aut Janusz Rybakowski verfasserin aut Martin Schalling verfasserin aut Peter R. Schofield verfasserin aut Thomas G. Schulze verfasserin aut Giovanni Severino verfasserin aut Alessio Squassina verfasserin aut Julia Veeh verfasserin aut Eduard Vieta verfasserin aut Thomas Trappenberg verfasserin aut Martin Alda verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 10 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:10 https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c kostenfrei https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 10 |
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10.1038/s41598-020-80814-z doi (DE-627)DOAJ059716029 (DE-599)DOAJ3122c8062c9b4f39a9a4aa0e6220ae8c DE-627 ger DE-627 rakwb eng William Stone verfasserin aut Prediction of lithium response using genomic data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. Medicine R Science Q Abraham Nunes verfasserin aut Kazufumi Akiyama verfasserin aut Nirmala Akula verfasserin aut Raffaella Ardau verfasserin aut Jean-Michel Aubry verfasserin aut Lena Backlund verfasserin aut Michael Bauer verfasserin aut Frank Bellivier verfasserin aut Pablo Cervantes verfasserin aut Hsi-Chung Chen verfasserin aut Caterina Chillotti verfasserin aut Cristiana Cruceanu verfasserin aut Alexandre Dayer verfasserin aut Franziska Degenhardt verfasserin aut Maria Del Zompo verfasserin aut Andreas J. Forstner verfasserin aut Mark Frye verfasserin aut Janice M. Fullerton verfasserin aut Maria Grigoroiu-Serbanescu verfasserin aut Paul Grof verfasserin aut Ryota Hashimoto verfasserin aut Liping Hou verfasserin aut Esther Jiménez verfasserin aut Tadafumi Kato verfasserin aut John Kelsoe verfasserin aut Sarah Kittel-Schneider verfasserin aut Po-Hsiu Kuo verfasserin aut Ichiro Kusumi verfasserin aut Catharina Lavebratt verfasserin aut Mirko Manchia verfasserin aut Lina Martinsson verfasserin aut Manuel Mattheisen verfasserin aut Francis J. McMahon verfasserin aut Vincent Millischer verfasserin aut Philip B. Mitchell verfasserin aut Markus M. Nöthen verfasserin aut Claire O’Donovan verfasserin aut Norio Ozaki verfasserin aut Claudia Pisanu verfasserin aut Andreas Reif verfasserin aut Marcella Rietschel verfasserin aut Guy Rouleau verfasserin aut Janusz Rybakowski verfasserin aut Martin Schalling verfasserin aut Peter R. Schofield verfasserin aut Thomas G. Schulze verfasserin aut Giovanni Severino verfasserin aut Alessio Squassina verfasserin aut Julia Veeh verfasserin aut Eduard Vieta verfasserin aut Thomas Trappenberg verfasserin aut Martin Alda verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 10 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:10 https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c kostenfrei https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 10 |
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10.1038/s41598-020-80814-z doi (DE-627)DOAJ059716029 (DE-599)DOAJ3122c8062c9b4f39a9a4aa0e6220ae8c DE-627 ger DE-627 rakwb eng William Stone verfasserin aut Prediction of lithium response using genomic data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. Medicine R Science Q Abraham Nunes verfasserin aut Kazufumi Akiyama verfasserin aut Nirmala Akula verfasserin aut Raffaella Ardau verfasserin aut Jean-Michel Aubry verfasserin aut Lena Backlund verfasserin aut Michael Bauer verfasserin aut Frank Bellivier verfasserin aut Pablo Cervantes verfasserin aut Hsi-Chung Chen verfasserin aut Caterina Chillotti verfasserin aut Cristiana Cruceanu verfasserin aut Alexandre Dayer verfasserin aut Franziska Degenhardt verfasserin aut Maria Del Zompo verfasserin aut Andreas J. Forstner verfasserin aut Mark Frye verfasserin aut Janice M. Fullerton verfasserin aut Maria Grigoroiu-Serbanescu verfasserin aut Paul Grof verfasserin aut Ryota Hashimoto verfasserin aut Liping Hou verfasserin aut Esther Jiménez verfasserin aut Tadafumi Kato verfasserin aut John Kelsoe verfasserin aut Sarah Kittel-Schneider verfasserin aut Po-Hsiu Kuo verfasserin aut Ichiro Kusumi verfasserin aut Catharina Lavebratt verfasserin aut Mirko Manchia verfasserin aut Lina Martinsson verfasserin aut Manuel Mattheisen verfasserin aut Francis J. McMahon verfasserin aut Vincent Millischer verfasserin aut Philip B. Mitchell verfasserin aut Markus M. Nöthen verfasserin aut Claire O’Donovan verfasserin aut Norio Ozaki verfasserin aut Claudia Pisanu verfasserin aut Andreas Reif verfasserin aut Marcella Rietschel verfasserin aut Guy Rouleau verfasserin aut Janusz Rybakowski verfasserin aut Martin Schalling verfasserin aut Peter R. Schofield verfasserin aut Thomas G. Schulze verfasserin aut Giovanni Severino verfasserin aut Alessio Squassina verfasserin aut Julia Veeh verfasserin aut Eduard Vieta verfasserin aut Thomas Trappenberg verfasserin aut Martin Alda verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 10 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:10 https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c kostenfrei https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 10 |
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10.1038/s41598-020-80814-z doi (DE-627)DOAJ059716029 (DE-599)DOAJ3122c8062c9b4f39a9a4aa0e6220ae8c DE-627 ger DE-627 rakwb eng William Stone verfasserin aut Prediction of lithium response using genomic data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. Medicine R Science Q Abraham Nunes verfasserin aut Kazufumi Akiyama verfasserin aut Nirmala Akula verfasserin aut Raffaella Ardau verfasserin aut Jean-Michel Aubry verfasserin aut Lena Backlund verfasserin aut Michael Bauer verfasserin aut Frank Bellivier verfasserin aut Pablo Cervantes verfasserin aut Hsi-Chung Chen verfasserin aut Caterina Chillotti verfasserin aut Cristiana Cruceanu verfasserin aut Alexandre Dayer verfasserin aut Franziska Degenhardt verfasserin aut Maria Del Zompo verfasserin aut Andreas J. Forstner verfasserin aut Mark Frye verfasserin aut Janice M. Fullerton verfasserin aut Maria Grigoroiu-Serbanescu verfasserin aut Paul Grof verfasserin aut Ryota Hashimoto verfasserin aut Liping Hou verfasserin aut Esther Jiménez verfasserin aut Tadafumi Kato verfasserin aut John Kelsoe verfasserin aut Sarah Kittel-Schneider verfasserin aut Po-Hsiu Kuo verfasserin aut Ichiro Kusumi verfasserin aut Catharina Lavebratt verfasserin aut Mirko Manchia verfasserin aut Lina Martinsson verfasserin aut Manuel Mattheisen verfasserin aut Francis J. McMahon verfasserin aut Vincent Millischer verfasserin aut Philip B. Mitchell verfasserin aut Markus M. Nöthen verfasserin aut Claire O’Donovan verfasserin aut Norio Ozaki verfasserin aut Claudia Pisanu verfasserin aut Andreas Reif verfasserin aut Marcella Rietschel verfasserin aut Guy Rouleau verfasserin aut Janusz Rybakowski verfasserin aut Martin Schalling verfasserin aut Peter R. Schofield verfasserin aut Thomas G. Schulze verfasserin aut Giovanni Severino verfasserin aut Alessio Squassina verfasserin aut Julia Veeh verfasserin aut Eduard Vieta verfasserin aut Thomas Trappenberg verfasserin aut Martin Alda verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 10 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:10 https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c kostenfrei https://doi.org/10.1038/s41598-020-80814-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 10 |
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William Stone @@aut@@ Abraham Nunes @@aut@@ Kazufumi Akiyama @@aut@@ Nirmala Akula @@aut@@ Raffaella Ardau @@aut@@ Jean-Michel Aubry @@aut@@ Lena Backlund @@aut@@ Michael Bauer @@aut@@ Frank Bellivier @@aut@@ Pablo Cervantes @@aut@@ Hsi-Chung Chen @@aut@@ Caterina Chillotti @@aut@@ Cristiana Cruceanu @@aut@@ Alexandre Dayer @@aut@@ Franziska Degenhardt @@aut@@ Maria Del Zompo @@aut@@ Andreas J. Forstner @@aut@@ Mark Frye @@aut@@ Janice M. Fullerton @@aut@@ Maria Grigoroiu-Serbanescu @@aut@@ Paul Grof @@aut@@ Ryota Hashimoto @@aut@@ Liping Hou @@aut@@ Esther Jiménez @@aut@@ Tadafumi Kato @@aut@@ John Kelsoe @@aut@@ Sarah Kittel-Schneider @@aut@@ Po-Hsiu Kuo @@aut@@ Ichiro Kusumi @@aut@@ Catharina Lavebratt @@aut@@ Mirko Manchia @@aut@@ Lina Martinsson @@aut@@ Manuel Mattheisen @@aut@@ Francis J. McMahon @@aut@@ Vincent Millischer @@aut@@ Philip B. Mitchell @@aut@@ Markus M. Nöthen @@aut@@ Claire O’Donovan @@aut@@ Norio Ozaki @@aut@@ Claudia Pisanu @@aut@@ Andreas Reif @@aut@@ Marcella Rietschel @@aut@@ Guy Rouleau @@aut@@ Janusz Rybakowski @@aut@@ Martin Schalling @@aut@@ Peter R. Schofield @@aut@@ Thomas G. Schulze @@aut@@ Giovanni Severino @@aut@@ Alessio Squassina @@aut@@ Julia Veeh @@aut@@ Eduard Vieta @@aut@@ Thomas Trappenberg @@aut@@ Martin Alda @@aut@@ |
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Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. 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prediction of lithium response using genomic data |
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Prediction of lithium response using genomic data |
| abstract |
Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. |
| abstractGer |
Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. |
| abstract_unstemmed |
Abstract Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. |
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Prediction of lithium response using genomic data |
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https://doi.org/10.1038/s41598-020-80814-z https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c https://doaj.org/toc/2045-2322 |
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Abraham Nunes Kazufumi Akiyama Nirmala Akula Raffaella Ardau Jean-Michel Aubry Lena Backlund Michael Bauer Frank Bellivier Pablo Cervantes Hsi-Chung Chen Caterina Chillotti Cristiana Cruceanu Alexandre Dayer Franziska Degenhardt Maria Del Zompo Andreas J. Forstner Mark Frye Janice M. Fullerton Maria Grigoroiu-Serbanescu Paul Grof Ryota Hashimoto Liping Hou Esther Jiménez Tadafumi Kato John Kelsoe Sarah Kittel-Schneider Po-Hsiu Kuo Ichiro Kusumi Catharina Lavebratt Mirko Manchia Lina Martinsson Manuel Mattheisen Francis J. McMahon Vincent Millischer Philip B. Mitchell Markus M. Nöthen Claire O’Donovan Norio Ozaki Claudia Pisanu Andreas Reif Marcella Rietschel Guy Rouleau Janusz Rybakowski Martin Schalling Peter R. Schofield Thomas G. Schulze Giovanni Severino Alessio Squassina Julia Veeh Eduard Vieta Thomas Trappenberg Martin Alda |
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Abraham Nunes Kazufumi Akiyama Nirmala Akula Raffaella Ardau Jean-Michel Aubry Lena Backlund Michael Bauer Frank Bellivier Pablo Cervantes Hsi-Chung Chen Caterina Chillotti Cristiana Cruceanu Alexandre Dayer Franziska Degenhardt Maria Del Zompo Andreas J. Forstner Mark Frye Janice M. Fullerton Maria Grigoroiu-Serbanescu Paul Grof Ryota Hashimoto Liping Hou Esther Jiménez Tadafumi Kato John Kelsoe Sarah Kittel-Schneider Po-Hsiu Kuo Ichiro Kusumi Catharina Lavebratt Mirko Manchia Lina Martinsson Manuel Mattheisen Francis J. McMahon Vincent Millischer Philip B. Mitchell Markus M. Nöthen Claire O’Donovan Norio Ozaki Claudia Pisanu Andreas Reif Marcella Rietschel Guy Rouleau Janusz Rybakowski Martin Schalling Peter R. Schofield Thomas G. Schulze Giovanni Severino Alessio Squassina Julia Veeh Eduard Vieta Thomas Trappenberg Martin Alda |
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10.1038/s41598-020-80814-z |
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| score |
7.400139 |